Abstract:

 Under-sampling was easy to ignore some useful information of the majority class in imbalanced data sets classification. So, we propose an AdaBoost-based under-sampling ensemble approach U-Ensemble to solve this problem. Firstly, AdaBoost was used to process the imbalanced data sets in order to get the weights of samples. Then, we used Bagging as the classifier, bootstrap was no longer used when sampled the majority class, but we randomly selected some of samples that had larger and smaller weights.Meanwhile, we ensured that the number of the samples selected from the majority class were equal to the number of the minority class. At last, we combined the sampled majority class samples and all the minority class samples as the training data set for a component classifier. Experimental results showed the effectiveness of U-Ensemble.

Key words:  imbalanced data sets, AdaBoost, Under-sampling